nets_factory.py 7.26 KB
Newer Older
1
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains a factory for building various models."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools

import tensorflow as tf

from nets import alexnet
from nets import cifarnet
26
from nets import i3d
27
28
from nets import inception
from nets import lenet
andrewghoward's avatar
andrewghoward committed
29
from nets import mobilenet_v1
30
31
32
from nets import overfeat
from nets import resnet_v1
from nets import resnet_v2
33
from nets import s3dg
34
from nets import vgg
35
from nets.mobilenet import mobilenet_v2
36
from nets.nasnet import nasnet
maximneumann's avatar
maximneumann committed
37
from nets.nasnet import pnasnet
38

39

40
41
42
43
44
45
46
47
48
49
50
slim = tf.contrib.slim

networks_map = {'alexnet_v2': alexnet.alexnet_v2,
                'cifarnet': cifarnet.cifarnet,
                'overfeat': overfeat.overfeat,
                'vgg_a': vgg.vgg_a,
                'vgg_16': vgg.vgg_16,
                'vgg_19': vgg.vgg_19,
                'inception_v1': inception.inception_v1,
                'inception_v2': inception.inception_v2,
                'inception_v3': inception.inception_v3,
Alex Kurakin's avatar
Alex Kurakin committed
51
                'inception_v4': inception.inception_v4,
52
                'inception_resnet_v2': inception.inception_resnet_v2,
53
54
                'i3d': i3d.i3d,
                's3dg': s3dg.s3dg,
55
56
57
58
59
60
61
62
63
                'lenet': lenet.lenet,
                'resnet_v1_50': resnet_v1.resnet_v1_50,
                'resnet_v1_101': resnet_v1.resnet_v1_101,
                'resnet_v1_152': resnet_v1.resnet_v1_152,
                'resnet_v1_200': resnet_v1.resnet_v1_200,
                'resnet_v2_50': resnet_v2.resnet_v2_50,
                'resnet_v2_101': resnet_v2.resnet_v2_101,
                'resnet_v2_152': resnet_v2.resnet_v2_152,
                'resnet_v2_200': resnet_v2.resnet_v2_200,
andrewghoward's avatar
andrewghoward committed
64
                'mobilenet_v1': mobilenet_v1.mobilenet_v1,
Pete Warden's avatar
Pete Warden committed
65
66
67
                'mobilenet_v1_075': mobilenet_v1.mobilenet_v1_075,
                'mobilenet_v1_050': mobilenet_v1.mobilenet_v1_050,
                'mobilenet_v1_025': mobilenet_v1.mobilenet_v1_025,
68
                'mobilenet_v2': mobilenet_v2.mobilenet,
69
70
                'mobilenet_v2_140': mobilenet_v2.mobilenet_v2_140,
                'mobilenet_v2_035': mobilenet_v2.mobilenet_v2_035,
71
72
73
                'nasnet_cifar': nasnet.build_nasnet_cifar,
                'nasnet_mobile': nasnet.build_nasnet_mobile,
                'nasnet_large': nasnet.build_nasnet_large,
maximneumann's avatar
maximneumann committed
74
                'pnasnet_large': pnasnet.build_pnasnet_large,
75
                'pnasnet_mobile': pnasnet.build_pnasnet_mobile,
76
77
78
79
80
81
82
83
84
85
86
               }

arg_scopes_map = {'alexnet_v2': alexnet.alexnet_v2_arg_scope,
                  'cifarnet': cifarnet.cifarnet_arg_scope,
                  'overfeat': overfeat.overfeat_arg_scope,
                  'vgg_a': vgg.vgg_arg_scope,
                  'vgg_16': vgg.vgg_arg_scope,
                  'vgg_19': vgg.vgg_arg_scope,
                  'inception_v1': inception.inception_v3_arg_scope,
                  'inception_v2': inception.inception_v3_arg_scope,
                  'inception_v3': inception.inception_v3_arg_scope,
Alex Kurakin's avatar
Alex Kurakin committed
87
                  'inception_v4': inception.inception_v4_arg_scope,
88
89
                  'inception_resnet_v2':
                  inception.inception_resnet_v2_arg_scope,
90
91
                  'i3d': i3d.i3d_arg_scope,
                  's3dg': s3dg.s3dg_arg_scope,
92
93
94
95
96
97
98
99
100
                  'lenet': lenet.lenet_arg_scope,
                  'resnet_v1_50': resnet_v1.resnet_arg_scope,
                  'resnet_v1_101': resnet_v1.resnet_arg_scope,
                  'resnet_v1_152': resnet_v1.resnet_arg_scope,
                  'resnet_v1_200': resnet_v1.resnet_arg_scope,
                  'resnet_v2_50': resnet_v2.resnet_arg_scope,
                  'resnet_v2_101': resnet_v2.resnet_arg_scope,
                  'resnet_v2_152': resnet_v2.resnet_arg_scope,
                  'resnet_v2_200': resnet_v2.resnet_arg_scope,
andrewghoward's avatar
andrewghoward committed
101
                  'mobilenet_v1': mobilenet_v1.mobilenet_v1_arg_scope,
Pete Warden's avatar
Pete Warden committed
102
103
104
                  'mobilenet_v1_075': mobilenet_v1.mobilenet_v1_arg_scope,
                  'mobilenet_v1_050': mobilenet_v1.mobilenet_v1_arg_scope,
                  'mobilenet_v1_025': mobilenet_v1.mobilenet_v1_arg_scope,
105
                  'mobilenet_v2': mobilenet_v2.training_scope,
106
107
                  'mobilenet_v2_035': mobilenet_v2.training_scope,
                  'mobilenet_v2_140': mobilenet_v2.training_scope,
108
109
110
                  'nasnet_cifar': nasnet.nasnet_cifar_arg_scope,
                  'nasnet_mobile': nasnet.nasnet_mobile_arg_scope,
                  'nasnet_large': nasnet.nasnet_large_arg_scope,
maximneumann's avatar
maximneumann committed
111
                  'pnasnet_large': pnasnet.pnasnet_large_arg_scope,
112
                  'pnasnet_mobile': pnasnet.pnasnet_mobile_arg_scope,
113
114
115
116
117
118
119
120
                 }


def get_network_fn(name, num_classes, weight_decay=0.0, is_training=False):
  """Returns a network_fn such as `logits, end_points = network_fn(images)`.

  Args:
    name: The name of the network.
121
122
    num_classes: The number of classes to use for classification. If 0 or None,
      the logits layer is omitted and its input features are returned instead.
123
124
125
126
127
128
129
    weight_decay: The l2 coefficient for the model weights.
    is_training: `True` if the model is being used for training and `False`
      otherwise.

  Returns:
    network_fn: A function that applies the model to a batch of images. It has
      the following signature:
130
131
132
133
134
135
136
137
138
139
140
141
142
143
          net, end_points = network_fn(images)
      The `images` input is a tensor of shape [batch_size, height, width, 3]
      with height = width = network_fn.default_image_size. (The permissibility
      and treatment of other sizes depends on the network_fn.)
      The returned `end_points` are a dictionary of intermediate activations.
      The returned `net` is the topmost layer, depending on `num_classes`:
      If `num_classes` was a non-zero integer, `net` is a logits tensor
      of shape [batch_size, num_classes].
      If `num_classes` was 0 or `None`, `net` is a tensor with the input
      to the logits layer of shape [batch_size, 1, 1, num_features] or
      [batch_size, num_features]. Dropout has not been applied to this
      (even if the network's original classification does); it remains for
      the caller to do this or not.

144
145
146
147
148
149
150
  Raises:
    ValueError: If network `name` is not recognized.
  """
  if name not in networks_map:
    raise ValueError('Name of network unknown %s' % name)
  func = networks_map[name]
  @functools.wraps(func)
151
  def network_fn(images, **kwargs):
152
    arg_scope = arg_scopes_map[name](weight_decay=weight_decay)
153
    with slim.arg_scope(arg_scope):
154
155
      return func(images, num_classes=num_classes, is_training=is_training,
                  **kwargs)
156
157
158
159
  if hasattr(func, 'default_image_size'):
    network_fn.default_image_size = func.default_image_size

  return network_fn